2024 Fellow

Xunbi Ji

Department of Mechanical Engineering

Xunbi Ji is a Ph.D. candidate in the Department of Mechanical Engineering. She is advised by Professor Gabor Orosz. Her research goal is to advance data-driven methods for learning delayed dynamical systems and apply the methods to intelligent vehicles for improving automotive safety. She has been developing machine learning tools to identify the time delays and the nonlinearities simultaneously in dynamical systems. The proposed framework sheds lights on reducing the complexity of neural networks and improves the interpretability of data-driven models. She also studies the effects of time delays on the vehicle safety and performance through both analytical and numerical methods. Substantial delays can greatly compromise control performance, leading to unsafe behaviors. Many control strategies designed for time delay systems require good model and accurate estimation of the delays. She aims at addressing the safety issue posed by the delays in the automotive control tasks, through learning the delays and vehicle models from trajectory data.

Besides the research work, Xunbi worked as a graduate student instructor for multiple courses. She is a member of Tau Beta Pi engineering honor society and a member of Qingyun Chinese Music Ensemble. She participated in the ensemble performances supported by the Diversity, Equity, and Inclusion (DEI) 2.0 Initiative.